docstr: Determine what arguments we need to procure for a mean matching function.
  
  Parameters
  ----------
  objective: str
      The objective of the model that will create the candidate
      and bachelor predictions.
  
  Returns
  -------
  list of arguments required by this objectives mean matching function.
docstr_tok: ["Determine","what","arguments","we","need","to","procure","for","a","mean","matching","function",".","Parameters","--","--","--","--","--","objective",":","str","The","objective","of","the","model","that","will","create","the","candidate","and","bachelor","predictions",".","Returns","--","--","--","-","list","of","arguments","required","by","this","objectives","mean","matching","function","."]
code: def get_mean_match_args(self, objective):
      """
      Determine what arguments we need to procure for a mean matching function.
  
      Parameters
      ----------
      objective: str
          The objective of the model that will create the candidate
          and bachelor predictions.
  
      Returns
      -------
      list of arguments required by this objectives mean matching function.
      """
      try:
          obj_args = self.objective_args[objective]
  
      except:
          raise ValueError(
              f"Could not get mean matching args for objective {objective}"
              + "Add this objective to the MeanMatchSchema by using .set_mean_match_function()"
          )
  
      return obj_args
  
file_path: miceforest/miceforest/MeanMatchScheme.py
imports: from .compat import pd_DataFrame
  import inspect
  from copy import deepcopy
  from typing import Callable, Union, Dict, Set
  from numpy import dtype
label_desc: Use image node_id 11 for calling the MeanMatchScheme obj's underlying member method code with example usage: obj.get_mean_match_args(objective) and returns: obj_args